*3.3. Statistical Components*

Statistics is a variable used to analyze and test data in statistical theory. It is the macro performance of data in the statistical domain. This paper creatively applied statistical components as one of the dual channels in the deep model to extract more features. The input matrix of raw time series we already defined as Equation (2). Each raw sample *x*(*t*)*Input* corresponds to six tuples named *Stati*stics, which contains *mean*, *max*, *min*, standard deviation (*Sd*), skewness (*Skew*), and kurtosis (*Kurt*), which are defined as Equations (13)–(18).

$$
gamma(t) = \frac{1}{M} \sum\_{t=1}^{M} \mathbf{x}(t) \tag{13}
$$

$$\max(t) = \max(\mathbf{x}(t))\tag{14}$$

$$\min(t) = \min(\mathbf{x}(t))\tag{15}$$

$$Sd(t) = \sqrt{\frac{1}{M} \sum\_{t=1}^{M} (\mathbf{x}(t) - mean(t))^2} \tag{16}$$

$$Sk x w(t) = E\left[ \left( \frac{\mathbf{x}(t) - m \mathbf{e} m(t)}{s d(t)} \right)^3 \right] \tag{17}$$

$$Kurt(t) = E\left[\left(\frac{\mathbf{x}(t) - mean(t)}{sd(t)}\right)^4\right] \tag{18}$$
